# A tibble: 6 × 8
season episode dialogue_density avg_length sentiment_variance unique_words
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 0.930 37.5 3.32 960
2 1 2 0.994 33.8 3.99 950
3 1 3 0.992 31.1 4.08 915
4 1 4 0.994 32.2 3.71 892
5 1 5 0.994 34.1 3.78 888
6 1 6 0.994 33.2 3.30 921
# ℹ 2 more variables: question_ratio <dbl>, exclamation_ratio <dbl>Final Presentation
5/8/25
Project 1 - Data Visualization
Two TidyTuesday datasets
Data on Bob’s Burgers Dialogue from various episodes.
Data on how Horror Movies performed in the box office/how much the public enjoyed them.
American Sitcom with 15 seasons
Follows Bob Belcher and his family, who own the restaurant Bob’s Burgers
Explored a possible relationship between the amount of lines that ended with questions versus exclamations
No clear trends from scatterplot, more questions does not mean more excitement in an epsiode.
Popular genre of movies meant to evoke feelings of fear and terror
Since 2017, around 40+ horror movies are released each year
# A tibble: 6 × 20 id original_title title original_language overview tagline release_date <dbl> <chr> <chr> <chr> <chr> <chr> <date> 1 760161 Orphan: First K… Orph… en After e… There'… 2022-07-27 2 760741 Beast Beast en A recen… Fight … 2022-08-11 3 882598 Smile Smile en After w… Once y… 2022-09-23 4 756999 The Black Phone The … en Finney … Never … 2022-06-22 5 772450 Presencias Pres… es A man w… <NA> 2022-09-07 6 1014226 Sonríe Sonr… es <NA> <NA> 2022-08-18 # ℹ 13 more variables: poster_path <chr>, popularity <dbl>, vote_count <dbl>, # vote_average <dbl>, budget <dbl>, revenue <dbl>, runtime <dbl>, # status <chr>, adult <lgl>, backdrop_path <chr>, genre_names <chr>, # collection <dbl>, collection_name <chr>Explored relationship between a movie’s budget and revenue
No clear trends from scatterplot, spending more money does not necessarily mean you will make more money.
Has movie duration changed over time?
Data from the mid 1900s to 2022.
Films from multiple countries and genres on Netflix.
Manipulated data to include only movies that have a runtime of a hour or longer.
movies_only <- netflix_titles %>% filter(type == "Movie") %>% mutate(movie_duration = as.numeric(str_extract(duration, "\\d+"))) %>% filter(!is.na(duration) & movie_duration >= 60)%>% arrange(release_year)%>% select(type, release_year, duration, movie_duration) head(movies_only)# A tibble: 6 × 4 type release_year duration movie_duration <chr> <dbl> <chr> <dbl> 1 Movie 1943 61 min 61 2 Movie 1943 82 min 82 3 Movie 1944 76 min 76 4 Movie 1945 63 min 63 5 Movie 1954 116 min 116 6 Movie 1954 120 min 120Found no trend, movie durations have stayed in a similar range over the years.
Words preceding ‘of’
Dataset included titles of many different works of media.
Analyzed which words most commonly came before ‘of’ in a title.
Chose ‘of’ because of how common it is used - ex: story of, legend of, etc.
words <- netflix_titles %>% mutate(of = str_extract(str_to_lower(title), "\\b\\w+(?= of)")) %>% filter(!is.na(of)) %>% group_by(of) %>% summarise(n = n()) %>% arrange(desc(n)) %>% slice_head(n = 10) head(words)# A tibble: 6 × 2 of n <chr> <int> 1 legend 19 2 story 19 3 secrets 16 4 life 11 5 tales 11 6 age 8Most common words were (as predicted) legend, story.
Similar movie title formats are recycled.
